Cover Image

L^1 data fitting for Inverse Problems yields optimal rates of convergence in case of discretized white Gaussian noise

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bätz, Kristina, Werner, Frank
Format:	Preprint
Published:	2025
Subjects:	Numerical Analysis Primary 62G05, Secondary 65J22
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Empirical Risk Minimization as Parameter Choice Rule for General Linear Regularization Methods
by: Li, Housen, et al.
Published: (2017)

Hybrid ABBA-GMRES for Unmatched Backprojectors in Large Scale X-Ray Computerized Tomography
by: Bentley, Ryan, et al.
Published: (2026)

How Regularization Terms Make Invertible Neural Networks Bayesian Point Estimators
by: Heilenkötter, Nick
Published: (2025)

Continuous-discrete smoothing of diffusions
by: Mider, Marcin, et al.
Published: (2017)

An invitation to adaptive Markov chain Monte Carlo convergence theory
by: Laitinen, Pietari, et al.
Published: (2024)

A discrete Saint-Venant principle for finite element discretizations of elliptic problems
by: Buchholz, Tim, et al.
Published: (2026)

Asymptotic and pre-asymptotic convergence of sparse grids for anisotropic kernel interpolation
by: Addy, Elliot J., et al.
Published: (2026)

Robust optimal design of large-scale Bayesian nonlinear inverse problems
by: Chowdhary, Abhijit, et al.
Published: (2024)

Convergence analysis of Tikhonov regularization for non-linear statistical inverse learning problems
by: Rastogi, Abhishake, et al.
Published: (2019)

A Scalable Sequential Framework for Dynamic Inverse Problems via Model Parameter Estimation
by: Keating, Aryeh, et al.
Published: (2026)

Towards optimal sensor placement for inverse problems in spaces of measures
by: Huynh, Phuoc-Truong, et al.
Published: (2023)

An extended latent factor framework for ill-posed linear regression
by: Finocchio, Gianluca, et al.
Published: (2023)

Maximum a posteriori testing in statistical inverse problems
by: Kretschmann, Remo, et al.
Published: (2024)

Bounds for the sampling discretization error and their applications to the universal sampling discretization
by: Kosov, E. D., et al.
Published: (2023)

Optimal convergence rates of MCMC integration for functions with unbounded second moment
by: Hofstadler, Julian
Published: (2024)

Hypertrees and their host trees: a survey
by: Di Fonzo, Pablo De Caria
Published: (2025)

Multilevel Markov Chain Monte Carlo with likelihood scaling for Bayesian inversion with high-resolution observations
by: Vanmechelen, Pieter, et al.
Published: (2024)

Understanding and Resolving Singularities in 3D Dirichlet Boundary Problems
by: Levin, David
Published: (2026)

Amortized Energy-Based Bayesian Inference
by: Kaveh, Hojjat, et al.
Published: (2026)

Time-complexity of sampling from a multimodal distribution using sequential Monte Carlo
by: Han, Ruiyu, et al.
Published: (2025)

Convergence of a Sequential Monte Carlo algorithm towards multimodal distributions on Rd
by: Han, Ruiyu
Published: (2025)

On identification in ill-posed linear regression
by: Finocchio, Gianluca, et al.
Published: (2025)

Fast Mixing of Data Augmentation Algorithms: Bayesian Probit, Logit, and Lasso Regression
by: Lee, Holden, et al.
Published: (2024)

Quantitative convergence of a discretization of dynamic optimal transport using the dual formulation
by: Ishida, Sadashige, et al.
Published: (2023)

A parallel domain decomposition method for solving elliptic equations on manifolds
by: Qin, Lizhen, et al.
Published: (2024)

Decoupling for Markov Chains
by: Bou-Rabee, Nawaf, et al.
Published: (2025)

On sample complexity for covariance estimation via the unadjusted Langevin algorithm
by: Nakakita, Shogo
Published: (2026)

A symmetric function approach to polynomial regression
by: Herbig, Hans-Christian, et al.
Published: (2024)

One Operator for Many Densities: Amortized Approximation of Conditioning by Neural Operators
by: Tsimpos, Panos, et al.
Published: (2026)

Spectral Regularized Kernel Two-Sample Tests
by: Hagrass, Omar, et al.
Published: (2022)

Minimax Optimal Kernel Two-Sample Tests with Random Features
by: Mukherjee, Soumya, et al.
Published: (2025)

Spectral Regularized Kernel Goodness-of-Fit Tests
by: Hagrass, Omar, et al.
Published: (2023)

Minimax Optimal Goodness-of-Fit Testing with Kernel Stein Discrepancy
by: Hagrass, Omar, et al.
Published: (2024)

A short tour of operator learning theory: Convergence rates, statistical limits, and open questions
by: Brugiapaglia, Simone, et al.
Published: (2026)

A spatial-correlated multitask linear mixed-effects model for imaging genetics
by: Pu, Zhibin, et al.
Published: (2024)

Non-parametric estimates for graphon mean-field particle systems
by: Bayraktar, Erhan, et al.
Published: (2024)

Pattern recovery by SLOPE
by: Bogdan, Małgorzata, et al.
Published: (2022)

Mixing time of the conditional backward sampling particle filter
by: Karjalainen, Joona, et al.
Published: (2023)

Optimal designs for discrete choice models via graph Laplacians
by: Röttger, Frank, et al.
Published: (2022)

On the cardinality of lower sets and universal discretization
by: Dai, F., et al.
Published: (2022)